Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting.

A patient-specific airflow simulation was developed to help address the pressing need for an expansion of the ventilator capacity in response to the COVID-19 pandemic. The computational model provides guidance regarding how to split a ventilator between two or more patients with differing respiratory physiologies. To address the need for fast deployment and identification of optimal patient-specific tuning, there was a need to simulate hundreds of millions of different clinically relevant parameter combinations in a short time. This task, driven by the dire circumstances, presented unique computational and research challenges. We present here the guiding principles and lessons learned as to how a large-scale and robust cloud instance was designed and deployed within 24 hours and 800 000 compute hours were utilized in a 72-hour period. We discuss the design choices to enable a quick turnaround of the model, execute the simulation, and create an intuitive and interactive interface.

A critical review of hydrological data collection for assessing preservation risk for urban waterlogged archaeology: A case study from the City of York, UK.

Environmental change caused by urban development, possibly augmented by climate change, may result in accelerated decay of in situ archaeological resources. Damage may be related to changes in hydrological processes. Such archaeological resources have to be considered in environmental planning. In this paper we highlight the need for improved hydrological data from urban archaeological sites using the case study of the City of York, UK, arguably one of the most well studied and well preserved urban archaeological environments globally. We suggest that the quality of hydrological data collected during routine surveys and experimental work must be improved and standardised in order for us to produce reliable archaeological risk models for urban sites.

Impact of hybrid fluorodeoxyglucose positron-emission tomography/computed tomography on radiotherapy planning in esophageal and non-small-cell lung cancer.

PURPOSE: The aim of this study was to investigate the impact of a hybrid fluorodeoxyglucose positron-emission tomography/computed tomography (FDG-PET/CT) scanner in radiotherapy planning for esophageal and non-small-cell lung cancer (NSCLC). METHODS AND MATERIALS: A total of 30 patients (16 with esophageal cancer, 14 with NSCLC) underwent an FDG-PET/CT for radiotherapy planning purposes. Noncontrast total-body spiral CT scans were obtained first, followed immediately by FDG-PET imaging which was automatically co-registered to the CT scan. A physician not involved in the patients' original treatment planning designed a gross tumor volume (GTV) based first on the CT dataset alone, while blinded to the FDG-PET dataset. Afterward, the physician designed a GTV based on the fused PET/CT dataset. To standardize PET GTV margin definition, background liver PET activity was standardized in all images. The CT-based and PET/CT-based GTVs were then quantitatively compared by way of an index of conformality, which is the ratio of the intersection of the two GTVs to their union. RESULTS: The mean index of conformality was 0.44 (range, 0.00-0.70) for patients with NSCLC and 0.46 (range, 0.13-0.80) for patients with esophageal cancer. In 10 of the 16 (62.5%) esophageal cancer patients, and in 12 of the 14 (85.7%) NSCLC patients, the addition of the FDG-PET data led to the definition of a smaller GTV. CONCLUSION: The incorporation of a hybrid FDG-PET/CT scanner had an impact on the radiotherapy planning of esophageal cancer and NSCLC. In future studies, we recommend adoption of a conformality index for a more comprehensive comparison of newer treatment planning imaging modalities to conventional options.